Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations3255
Missing cells3698
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory663.9 B

Variable types

Numeric7
DateTime2
Categorical6
Text2

Alerts

Bias Motive Description is highly overall correlated with Offense CategoryHigh correlation
Complaint Precinct Code is highly overall correlated with County and 1 other fieldsHigh correlation
Complaint Year Number is highly overall correlated with Full Complaint IDHigh correlation
County is highly overall correlated with Complaint Precinct Code and 1 other fieldsHigh correlation
Full Complaint ID is highly overall correlated with Complaint Year NumberHigh correlation
Law Code Category Description is highly overall correlated with Offense DescriptionHigh correlation
Offense Category is highly overall correlated with Bias Motive DescriptionHigh correlation
Offense Description is highly overall correlated with Law Code Category DescriptionHigh correlation
Patrol Borough Name is highly overall correlated with Complaint Precinct Code and 1 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
Unnamed: 0.1 is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0.2 is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Arrest Date has 1849 (56.8%) missing values Missing
Arrest Id has 1849 (56.8%) missing values Missing
Unnamed: 0.2 is uniformly distributed Uniform
Unnamed: 0.1 is uniformly distributed Uniform
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0.2 has unique values Unique
Unnamed: 0.1 has unique values Unique
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2025-02-28 21:31:10.222775
Analysis finished2025-02-28 21:31:12.887520
Duration2.66 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0.2
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct3255
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1627
Minimum0
Maximum3254
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2025-02-28T16:31:12.914585image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162.7
Q1813.5
median1627
Q32440.5
95-th percentile3091.3
Maximum3254
Range3254
Interquartile range (IQR)1627

Descriptive statistics

Standard deviation939.78189
Coefficient of variation (CV)0.5776164
Kurtosis-1.2
Mean1627
Median Absolute Deviation (MAD)814
Skewness0
Sum5295885
Variance883190
MonotonicityStrictly increasing
2025-02-28T16:31:12.952747image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
2174 1
 
< 0.1%
2164 1
 
< 0.1%
2165 1
 
< 0.1%
2166 1
 
< 0.1%
2167 1
 
< 0.1%
2168 1
 
< 0.1%
2169 1
 
< 0.1%
2170 1
 
< 0.1%
2171 1
 
< 0.1%
Other values (3245) 3245
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
3254 1
< 0.1%
3253 1
< 0.1%
3252 1
< 0.1%
3251 1
< 0.1%
3250 1
< 0.1%
3249 1
< 0.1%
3248 1
< 0.1%
3247 1
< 0.1%
3246 1
< 0.1%
3245 1
< 0.1%

Unnamed: 0.1
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct3255
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1627
Minimum0
Maximum3254
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2025-02-28T16:31:12.989252image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162.7
Q1813.5
median1627
Q32440.5
95-th percentile3091.3
Maximum3254
Range3254
Interquartile range (IQR)1627

Descriptive statistics

Standard deviation939.78189
Coefficient of variation (CV)0.5776164
Kurtosis-1.2
Mean1627
Median Absolute Deviation (MAD)814
Skewness0
Sum5295885
Variance883190
MonotonicityStrictly increasing
2025-02-28T16:31:13.028142image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
2174 1
 
< 0.1%
2164 1
 
< 0.1%
2165 1
 
< 0.1%
2166 1
 
< 0.1%
2167 1
 
< 0.1%
2168 1
 
< 0.1%
2169 1
 
< 0.1%
2170 1
 
< 0.1%
2171 1
 
< 0.1%
Other values (3245) 3245
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
3254 1
< 0.1%
3253 1
< 0.1%
3252 1
< 0.1%
3251 1
< 0.1%
3250 1
< 0.1%
3249 1
< 0.1%
3248 1
< 0.1%
3247 1
< 0.1%
3246 1
< 0.1%
3245 1
< 0.1%

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct3255
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1627
Minimum0
Maximum3254
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2025-02-28T16:31:13.065849image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162.7
Q1813.5
median1627
Q32440.5
95-th percentile3091.3
Maximum3254
Range3254
Interquartile range (IQR)1627

Descriptive statistics

Standard deviation939.78189
Coefficient of variation (CV)0.5776164
Kurtosis-1.2
Mean1627
Median Absolute Deviation (MAD)814
Skewness0
Sum5295885
Variance883190
MonotonicityStrictly increasing
2025-02-28T16:31:13.104329image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
2174 1
 
< 0.1%
2164 1
 
< 0.1%
2165 1
 
< 0.1%
2166 1
 
< 0.1%
2167 1
 
< 0.1%
2168 1
 
< 0.1%
2169 1
 
< 0.1%
2170 1
 
< 0.1%
2171 1
 
< 0.1%
Other values (3245) 3245
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
3254 1
< 0.1%
3253 1
< 0.1%
3252 1
< 0.1%
3251 1
< 0.1%
3250 1
< 0.1%
3249 1
< 0.1%
3248 1
< 0.1%
3247 1
< 0.1%
3246 1
< 0.1%
3245 1
< 0.1%

Full Complaint ID
Real number (ℝ)

High correlation 

Distinct2991
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0218455 × 1014
Minimum2.0190011 × 1014
Maximum2.0241231 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2025-02-28T16:31:13.142794image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum2.0190011 × 1014
5-th percentile2.0190481 × 1014
Q12.0210131 × 1014
median2.0220611 × 1014
Q32.0230766 × 1014
95-th percentile2.0240781 × 1014
Maximum2.0241231 × 1014
Range5.1220021 × 1011
Interquartile range (IQR)2.0635001 × 1011

Descriptive statistics

Standard deviation1.608058 × 1011
Coefficient of variation (CV)0.00079534167
Kurtosis-0.97355218
Mean2.0218455 × 1014
Median Absolute Deviation (MAD)1.0370029 × 1011
Skewness-0.33742337
Sum6.5811071 × 1017
Variance2.5858504 × 1022
MonotonicityNot monotonic
2025-02-28T16:31:13.182535image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.023109127 × 101411
 
0.3%
2.021018123 × 10149
 
0.3%
2.022090127 × 10146
 
0.2%
2.023018123 × 10145
 
0.2%
2.02207513 × 10145
 
0.2%
2.023010125 × 10145
 
0.2%
2.023075144 × 10144
 
0.1%
2.023060123 × 10144
 
0.1%
2.019066126 × 10144
 
0.1%
2.021025125 × 10144
 
0.1%
Other values (2981) 3198
98.2%
ValueCountFrequency (%)
2.019001121 × 10141
< 0.1%
2.019001121 × 10141
< 0.1%
2.019001122 × 10141
< 0.1%
2.019001122 × 10141
< 0.1%
2.019001122 × 10141
< 0.1%
2.019001123 × 10141
< 0.1%
2.019001124 × 10141
< 0.1%
2.019001124 × 10141
< 0.1%
2.019001125 × 10141
< 0.1%
2.019001126 × 10141
< 0.1%
ValueCountFrequency (%)
2.024123123 × 10141
< 0.1%
2.024123123 × 10141
< 0.1%
2.024123122 × 10141
< 0.1%
2.024122126 × 10141
< 0.1%
2.024122124 × 10141
< 0.1%
2.024122124 × 10141
< 0.1%
2.024122123 × 10141
< 0.1%
2.024122122 × 10141
< 0.1%
2.024121127 × 10141
< 0.1%
2.024121125 × 10141
< 0.1%

Complaint Year Number
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.7868
Minimum2019
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2025-02-28T16:31:13.211078image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum2019
5-th percentile2019
Q12021
median2022
Q32023
95-th percentile2024
Maximum2024
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6086001
Coefficient of variation (CV)0.00079563291
Kurtosis-0.97190774
Mean2021.7868
Median Absolute Deviation (MAD)1
Skewness-0.33944202
Sum6580916
Variance2.5875943
MonotonicityNot monotonic
2025-02-28T16:31:13.236715image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2023 755
23.2%
2022 672
20.6%
2021 578
17.8%
2024 519
15.9%
2019 447
13.7%
2020 284
 
8.7%
ValueCountFrequency (%)
2019 447
13.7%
2020 284
 
8.7%
2021 578
17.8%
2022 672
20.6%
2023 755
23.2%
2024 519
15.9%
ValueCountFrequency (%)
2024 519
15.9%
2023 755
23.2%
2022 672
20.6%
2021 578
17.8%
2020 284
 
8.7%
2019 447
13.7%

Month Number
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3004608
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2025-02-28T16:31:13.261702image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2917989
Coefficient of variation (CV)0.52246955
Kurtosis-1.1210351
Mean6.3004608
Median Absolute Deviation (MAD)3
Skewness0.1150436
Sum20508
Variance10.83594
MonotonicityNot monotonic
2025-02-28T16:31:13.288692image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 349
10.7%
3 344
10.6%
6 324
10.0%
4 275
8.4%
7 269
8.3%
9 269
8.3%
10 265
8.1%
2 253
7.8%
8 238
7.3%
11 230
7.1%
Other values (2) 439
13.5%
ValueCountFrequency (%)
1 225
6.9%
2 253
7.8%
3 344
10.6%
4 275
8.4%
5 349
10.7%
6 324
10.0%
7 269
8.3%
8 238
7.3%
9 269
8.3%
10 265
8.1%
ValueCountFrequency (%)
12 214
6.6%
11 230
7.1%
10 265
8.1%
9 269
8.3%
8 238
7.3%
7 269
8.3%
6 324
10.0%
5 349
10.7%
4 275
8.4%
3 344
10.6%
Distinct1447
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
Minimum2019-01-01 00:00:00
Maximum2024-09-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-28T16:31:13.324555image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:13.366896image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Complaint Precinct Code
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.571736
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2025-02-28T16:31:13.407639image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q119
median63
Q390
95-th percentile115
Maximum123
Range122
Interquartile range (IQR)71

Descriptive statistics

Standard deviation38.014809
Coefficient of variation (CV)0.64902992
Kurtosis-1.3738998
Mean58.571736
Median Absolute Deviation (MAD)39
Skewness0.04320368
Sum190651
Variance1445.1257
MonotonicityNot monotonic
2025-02-28T16:31:13.492834image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 134
 
4.1%
14 123
 
3.8%
19 115
 
3.5%
61 95
 
2.9%
18 95
 
2.9%
70 93
 
2.9%
71 88
 
2.7%
66 87
 
2.7%
13 86
 
2.6%
84 81
 
2.5%
Other values (67) 2258
69.4%
ValueCountFrequency (%)
1 81
2.5%
5 65
2.0%
6 71
2.2%
7 62
1.9%
9 36
 
1.1%
10 80
2.5%
13 86
2.6%
14 123
3.8%
17 50
1.5%
18 95
2.9%
ValueCountFrequency (%)
123 16
 
0.5%
122 37
1.1%
121 35
1.1%
120 34
1.0%
115 49
1.5%
114 62
1.9%
113 12
 
0.4%
112 62
1.9%
111 26
0.8%
110 46
1.4%

Patrol Borough Name
Categorical

High correlation 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size226.2 KiB
PATROL BORO MAN SOUTH
749 
PATROL BORO BKLYN SOUTH
639 
PATROL BORO BKLYN NORTH
491 
PATROL BORO MAN NORTH
471 
PATROL BORO QUEENS NORTH
371 
Other values (3)
534 

Length

Max length25
Median length24
Mean length22.135791
Min length17

Characters and Unicode

Total characters72052
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPATROL BORO BRONX
2nd rowPATROL BORO BRONX
3rd rowPATROL BORO BRONX
4th rowPATROL BORO BKLYN SOUTH
5th rowPATROL BORO BKLYN SOUTH

Common Values

ValueCountFrequency (%)
PATROL BORO MAN SOUTH 749
23.0%
PATROL BORO BKLYN SOUTH 639
19.6%
PATROL BORO BKLYN NORTH 491
15.1%
PATROL BORO MAN NORTH 471
14.5%
PATROL BORO QUEENS NORTH 371
11.4%
PATROL BORO QUEENS SOUTH 212
 
6.5%
PATROL BORO BRONX 200
 
6.1%
PATROL BORO STATEN ISLAND 122
 
3.7%

Length

2025-02-28T16:31:13.530841image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-28T16:31:13.570750image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
patrol 3255
25.4%
boro 3255
25.4%
south 1600
12.5%
north 1333
10.4%
man 1220
 
9.5%
bklyn 1130
 
8.8%
queens 583
 
4.5%
bronx 200
 
1.6%
staten 122
 
1.0%
island 122
 
1.0%

Most occurring characters

ValueCountFrequency (%)
O 12898
17.9%
9565
13.3%
R 8043
11.2%
T 6432
8.9%
A 4719
 
6.5%
N 4710
 
6.5%
B 4585
 
6.4%
L 4507
 
6.3%
P 3255
 
4.5%
H 2933
 
4.1%
Other values (10) 10405
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 12898
17.9%
9565
13.3%
R 8043
11.2%
T 6432
8.9%
A 4719
 
6.5%
N 4710
 
6.5%
B 4585
 
6.4%
L 4507
 
6.3%
P 3255
 
4.5%
H 2933
 
4.1%
Other values (10) 10405
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 12898
17.9%
9565
13.3%
R 8043
11.2%
T 6432
8.9%
A 4719
 
6.5%
N 4710
 
6.5%
B 4585
 
6.4%
L 4507
 
6.3%
P 3255
 
4.5%
H 2933
 
4.1%
Other values (10) 10405
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 12898
17.9%
9565
13.3%
R 8043
11.2%
T 6432
8.9%
A 4719
 
6.5%
N 4710
 
6.5%
B 4585
 
6.4%
L 4507
 
6.3%
P 3255
 
4.5%
H 2933
 
4.1%
Other values (10) 10405
14.4%

County
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size176.3 KiB
NEW YORK
1220 
KINGS
1130 
QUEENS
583 
BRONX
200 
RICHMOND
 
122

Length

Max length8
Median length6
Mean length6.4159754
Min length5

Characters and Unicode

Total characters20884
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRONX
2nd rowBRONX
3rd rowBRONX
4th rowKINGS
5th rowKINGS

Common Values

ValueCountFrequency (%)
NEW YORK 1220
37.5%
KINGS 1130
34.7%
QUEENS 583
17.9%
BRONX 200
 
6.1%
RICHMOND 122
 
3.7%

Length

2025-02-28T16:31:13.614679image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-28T16:31:13.638240image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
new 1220
27.3%
york 1220
27.3%
kings 1130
25.3%
queens 583
13.0%
bronx 200
 
4.5%
richmond 122
 
2.7%

Most occurring characters

ValueCountFrequency (%)
N 3255
15.6%
E 2386
11.4%
K 2350
11.3%
S 1713
8.2%
O 1542
7.4%
R 1542
7.4%
I 1252
 
6.0%
W 1220
 
5.8%
1220
 
5.8%
Y 1220
 
5.8%
Other values (9) 3184
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 3255
15.6%
E 2386
11.4%
K 2350
11.3%
S 1713
8.2%
O 1542
7.4%
R 1542
7.4%
I 1252
 
6.0%
W 1220
 
5.8%
1220
 
5.8%
Y 1220
 
5.8%
Other values (9) 3184
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 3255
15.6%
E 2386
11.4%
K 2350
11.3%
S 1713
8.2%
O 1542
7.4%
R 1542
7.4%
I 1252
 
6.0%
W 1220
 
5.8%
1220
 
5.8%
Y 1220
 
5.8%
Other values (9) 3184
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 3255
15.6%
E 2386
11.4%
K 2350
11.3%
S 1713
8.2%
O 1542
7.4%
R 1542
7.4%
I 1252
 
6.0%
W 1220
 
5.8%
1220
 
5.8%
Y 1220
 
5.8%
Other values (9) 3184
15.2%

Law Code Category Description
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size182.9 KiB
FELONY
1618 
MISDEMEANOR
1612 
VIOLATION
 
23
INVESTIGATION
 
2

Length

Max length13
Median length11
Mean length8.5016897
Min length6

Characters and Unicode

Total characters27673
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFELONY
2nd rowFELONY
3rd rowFELONY
4th rowFELONY
5th rowMISDEMEANOR

Common Values

ValueCountFrequency (%)
FELONY 1618
49.7%
MISDEMEANOR 1612
49.5%
VIOLATION 23
 
0.7%
INVESTIGATION 2
 
0.1%

Length

2025-02-28T16:31:13.670643image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-28T16:31:13.692515image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
felony 1618
49.7%
misdemeanor 1612
49.5%
violation 23
 
0.7%
investigation 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 4844
17.5%
O 3278
11.8%
N 3257
11.8%
M 3224
11.7%
I 1664
 
6.0%
L 1641
 
5.9%
A 1637
 
5.9%
F 1618
 
5.8%
Y 1618
 
5.8%
S 1614
 
5.8%
Other values (5) 3278
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4844
17.5%
O 3278
11.8%
N 3257
11.8%
M 3224
11.7%
I 1664
 
6.0%
L 1641
 
5.9%
A 1637
 
5.9%
F 1618
 
5.8%
Y 1618
 
5.8%
S 1614
 
5.8%
Other values (5) 3278
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4844
17.5%
O 3278
11.8%
N 3257
11.8%
M 3224
11.7%
I 1664
 
6.0%
L 1641
 
5.9%
A 1637
 
5.9%
F 1618
 
5.8%
Y 1618
 
5.8%
S 1614
 
5.8%
Other values (5) 3278
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4844
17.5%
O 3278
11.8%
N 3257
11.8%
M 3224
11.7%
I 1664
 
6.0%
L 1641
 
5.9%
A 1637
 
5.9%
F 1618
 
5.8%
Y 1618
 
5.8%
S 1614
 
5.8%
Other values (5) 3278
11.8%

Offense Description
Categorical

High correlation 

Distinct23
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size233.0 KiB
MISCELLANEOUS PENAL LAW
836 
CRIMINAL MISCHIEF & RELATED OF
670 
ASSAULT 3 & RELATED OFFENSES
654 
OFF. AGNST PUB ORD SENSBLTY &
446 
FELONY ASSAULT
394 
Other values (18)
255 

Length

Max length30
Median length28
Mean length24.245776
Min length4

Characters and Unicode

Total characters78920
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowBURGLARY
2nd rowMISCELLANEOUS PENAL LAW
3rd rowFELONY ASSAULT
4th rowMURDER & NON-NEGL. MANSLAUGHTE
5th rowOFF. AGNST PUB ORD SENSBLTY &

Common Values

ValueCountFrequency (%)
MISCELLANEOUS PENAL LAW 836
25.7%
CRIMINAL MISCHIEF & RELATED OF 670
20.6%
ASSAULT 3 & RELATED OFFENSES 654
20.1%
OFF. AGNST PUB ORD SENSBLTY & 446
13.7%
FELONY ASSAULT 394
12.1%
ROBBERY 108
 
3.3%
GRAND LARCENY 43
 
1.3%
HARRASSMENT 2 23
 
0.7%
PETIT LARCENY 20
 
0.6%
SEX CRIMES 15
 
0.5%
Other values (13) 46
 
1.4%

Length

2025-02-28T16:31:13.723135image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1778
13.7%
related 1325
 
10.2%
assault 1048
 
8.1%
miscellaneous 836
 
6.4%
law 836
 
6.4%
penal 836
 
6.4%
criminal 672
 
5.2%
mischief 670
 
5.2%
of 670
 
5.2%
offenses 661
 
5.1%
Other values (35) 3668
28.2%

Most occurring characters

ValueCountFrequency (%)
9745
12.3%
E 8287
10.5%
L 7331
 
9.3%
A 7267
 
9.2%
S 7223
 
9.2%
N 4490
 
5.7%
F 3951
 
5.0%
O 3594
 
4.6%
I 3587
 
4.5%
T 3355
 
4.3%
Other values (21) 20090
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9745
12.3%
E 8287
10.5%
L 7331
 
9.3%
A 7267
 
9.2%
S 7223
 
9.2%
N 4490
 
5.7%
F 3951
 
5.0%
O 3594
 
4.6%
I 3587
 
4.5%
T 3355
 
4.3%
Other values (21) 20090
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9745
12.3%
E 8287
10.5%
L 7331
 
9.3%
A 7267
 
9.2%
S 7223
 
9.2%
N 4490
 
5.7%
F 3951
 
5.0%
O 3594
 
4.6%
I 3587
 
4.5%
T 3355
 
4.3%
Other values (21) 20090
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9745
12.3%
E 8287
10.5%
L 7331
 
9.3%
A 7267
 
9.2%
S 7223
 
9.2%
N 4490
 
5.7%
F 3951
 
5.0%
O 3594
 
4.6%
I 3587
 
4.5%
T 3355
 
4.3%
Other values (21) 20090
25.5%
Distinct82
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size227.2 KiB
2025-02-28T16:31:13.809932image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length60
Median length54
Mean length22.437481
Min length6

Characters and Unicode

Total characters73034
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.9%

Sample

1st rowBURGLARY,UNCLASSIFIED,NIGHT
2nd rowAGGRAVATED HARASSMENT 1
3rd rowASSAULT 2,1,UNCLASSIFIED
4th rowMURDER,UNCLASSIFIED
5th rowAGGRAVATED HARASSMENT 2
ValueCountFrequency (%)
aggravated 1207
13.1%
harassment 1207
13.1%
assault 930
10.1%
1 770
 
8.4%
3 635
 
6.9%
criminal 566
 
6.2%
2 538
 
5.9%
mischief 387
 
4.2%
2,1,unclassified 383
 
4.2%
4th 334
 
3.6%
Other values (142) 2226
24.2%
2025-02-28T16:31:13.931163image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 10249
14.0%
S 6614
 
9.1%
6258
 
8.6%
I 4858
 
6.7%
E 4713
 
6.5%
T 4504
 
6.2%
R 4414
 
6.0%
N 3571
 
4.9%
G 3072
 
4.2%
M 2890
 
4.0%
Other values (28) 21891
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10249
14.0%
S 6614
 
9.1%
6258
 
8.6%
I 4858
 
6.7%
E 4713
 
6.5%
T 4504
 
6.2%
R 4414
 
6.0%
N 3571
 
4.9%
G 3072
 
4.2%
M 2890
 
4.0%
Other values (28) 21891
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10249
14.0%
S 6614
 
9.1%
6258
 
8.6%
I 4858
 
6.7%
E 4713
 
6.5%
T 4504
 
6.2%
R 4414
 
6.0%
N 3571
 
4.9%
G 3072
 
4.2%
M 2890
 
4.0%
Other values (28) 21891
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10249
14.0%
S 6614
 
9.1%
6258
 
8.6%
I 4858
 
6.7%
E 4713
 
6.5%
T 4504
 
6.2%
R 4414
 
6.0%
N 3571
 
4.9%
G 3072
 
4.2%
M 2890
 
4.0%
Other values (28) 21891
30.0%

Bias Motive Description
Categorical

High correlation 

Distinct28
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size199.4 KiB
ANTI-JEWISH
1504 
ANTI-MALE HOMOSEXUAL (GAY)
420 
ANTI-ASIAN
355 
ANTI-BLACK
257 
ANTI-OTHER ETHNICITY
 
133
Other values (23)
586 

Length

Max length33
Median length11
Mean length13.67404
Min length9

Characters and Unicode

Total characters44509
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowANTI-JEWISH
2nd rowANTI-JEWISH
3rd rowANTI-MALE HOMOSEXUAL (GAY)
4th rowANTI-ASIAN
5th rowANTI-JEWISH

Common Values

ValueCountFrequency (%)
ANTI-JEWISH 1504
46.2%
ANTI-MALE HOMOSEXUAL (GAY) 420
 
12.9%
ANTI-ASIAN 355
 
10.9%
ANTI-BLACK 257
 
7.9%
ANTI-OTHER ETHNICITY 133
 
4.1%
ANTI-MUSLIM 121
 
3.7%
ANTI-WHITE 110
 
3.4%
ANTI-TRANSGENDER 84
 
2.6%
ANTI-HISPANIC 77
 
2.4%
ANTI-CATHOLIC 57
 
1.8%
Other values (18) 137
 
4.2%

Length

2025-02-28T16:31:13.964918image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
anti-jewish 1504
34.5%
homosexual 457
 
10.5%
anti-male 421
 
9.7%
gay 420
 
9.6%
anti-asian 355
 
8.1%
anti-black 257
 
5.9%
anti-other 139
 
3.2%
ethnicity 133
 
3.1%
anti-muslim 121
 
2.8%
anti-white 110
 
2.5%
Other values (32) 439
 
10.1%

Most occurring characters

ValueCountFrequency (%)
I 5932
13.3%
A 5876
13.2%
N 4096
9.2%
T 3954
 
8.9%
- 3266
 
7.3%
E 3154
 
7.1%
S 2672
 
6.0%
H 2505
 
5.6%
W 1616
 
3.6%
J 1506
 
3.4%
Other values (20) 9932
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 5932
13.3%
A 5876
13.2%
N 4096
9.2%
T 3954
 
8.9%
- 3266
 
7.3%
E 3154
 
7.1%
S 2672
 
6.0%
H 2505
 
5.6%
W 1616
 
3.6%
J 1506
 
3.4%
Other values (20) 9932
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 5932
13.3%
A 5876
13.2%
N 4096
9.2%
T 3954
 
8.9%
- 3266
 
7.3%
E 3154
 
7.1%
S 2672
 
6.0%
H 2505
 
5.6%
W 1616
 
3.6%
J 1506
 
3.4%
Other values (20) 9932
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 5932
13.3%
A 5876
13.2%
N 4096
9.2%
T 3954
 
8.9%
- 3266
 
7.3%
E 3154
 
7.1%
S 2672
 
6.0%
H 2505
 
5.6%
W 1616
 
3.6%
J 1506
 
3.4%
Other values (20) 9932
22.3%

Offense Category
Categorical

High correlation 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size224.5 KiB
Religion/Religious Practice
1718 
Race/Color
724 
Sexual Orientation
466 
Ethnicity/National Origin/Ancestry
218 
Gender
 
119
Other values (3)
 
10

Length

Max length34
Median length27
Mean length21.581874
Min length3

Characters and Unicode

Total characters70249
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowReligion/Religious Practice
2nd rowReligion/Religious Practice
3rd rowSexual Orientation
4th rowRace/Color
5th rowReligion/Religious Practice

Common Values

ValueCountFrequency (%)
Religion/Religious Practice 1718
52.8%
Race/Color 724
22.2%
Sexual Orientation 466
 
14.3%
Ethnicity/National Origin/Ancestry 218
 
6.7%
Gender 119
 
3.7%
Unclassified 8
 
0.2%
Disability 1
 
< 0.1%
Age 1
 
< 0.1%

Length

2025-02-28T16:31:13.997635image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-28T16:31:14.025009image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
religion/religious 1718
30.4%
practice 1718
30.4%
race/color 724
12.8%
sexual 466
 
8.2%
orientation 466
 
8.2%
ethnicity/national 218
 
3.9%
origin/ancestry 218
 
3.9%
gender 119
 
2.1%
unclassified 8
 
0.1%
disability 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 10631
15.1%
e 7275
 
10.4%
o 5568
 
7.9%
l 4853
 
6.9%
c 4604
 
6.6%
R 4160
 
5.9%
a 3819
 
5.4%
g 3655
 
5.2%
n 3649
 
5.2%
t 3523
 
5.0%
Other values (21) 18512
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 10631
15.1%
e 7275
 
10.4%
o 5568
 
7.9%
l 4853
 
6.9%
c 4604
 
6.6%
R 4160
 
5.9%
a 3819
 
5.4%
g 3655
 
5.2%
n 3649
 
5.2%
t 3523
 
5.0%
Other values (21) 18512
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 10631
15.1%
e 7275
 
10.4%
o 5568
 
7.9%
l 4853
 
6.9%
c 4604
 
6.6%
R 4160
 
5.9%
a 3819
 
5.4%
g 3655
 
5.2%
n 3649
 
5.2%
t 3523
 
5.0%
Other values (21) 18512
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 10631
15.1%
e 7275
 
10.4%
o 5568
 
7.9%
l 4853
 
6.9%
c 4604
 
6.6%
R 4160
 
5.9%
a 3819
 
5.4%
g 3655
 
5.2%
n 3649
 
5.2%
t 3523
 
5.0%
Other values (21) 18512
26.4%

Arrest Date
Date

Missing 

Distinct745
Distinct (%)53.0%
Missing1849
Missing (%)56.8%
Memory size25.6 KiB
Minimum2019-01-06 00:00:00
Maximum2024-09-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-28T16:31:14.067800image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:14.111000image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Arrest Id
Text

Missing 

Distinct1395
Distinct (%)99.2%
Missing1849
Missing (%)56.8%
Memory size137.5 KiB
2025-02-28T16:31:14.222086image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters12654
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1387 ?
Unique (%)98.6%

Sample

1st rowB33683676
2nd rowB34705870
3rd rowB34707656
4th rowK31675023
5th rowK31679592
ValueCountFrequency (%)
k35674657 3
 
0.2%
k35674659 3
 
0.2%
k35674656 3
 
0.2%
k34719305 2
 
0.1%
k35733591 2
 
0.1%
k34719306 2
 
0.1%
k34719303 2
 
0.1%
b31682806 2
 
0.1%
k31710607 1
 
0.1%
k32697729 1
 
0.1%
Other values (1385) 1385
98.5%
2025-02-28T16:31:14.355957image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 2236
17.7%
6 1715
13.6%
7 1314
10.4%
8 928
7.3%
5 919
7.3%
4 874
 
6.9%
0 856
 
6.8%
1 836
 
6.6%
9 832
 
6.6%
2 738
 
5.8%
Other values (5) 1406
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2236
17.7%
6 1715
13.6%
7 1314
10.4%
8 928
7.3%
5 919
7.3%
4 874
 
6.9%
0 856
 
6.8%
1 836
 
6.6%
9 832
 
6.6%
2 738
 
5.8%
Other values (5) 1406
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2236
17.7%
6 1715
13.6%
7 1314
10.4%
8 928
7.3%
5 919
7.3%
4 874
 
6.9%
0 856
 
6.8%
1 836
 
6.6%
9 832
 
6.6%
2 738
 
5.8%
Other values (5) 1406
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2236
17.7%
6 1715
13.6%
7 1314
10.4%
8 928
7.3%
5 919
7.3%
4 874
 
6.9%
0 856
 
6.8%
1 836
 
6.6%
9 832
 
6.6%
2 738
 
5.8%
Other values (5) 1406
11.1%

Interactions

2025-02-28T16:31:12.230964image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:10.757514image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:10.995498image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.265301image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.578101image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.798877image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.017787image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.263133image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:10.795926image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.026417image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.311976image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.609878image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.830206image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.048741image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.294542image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:10.827244image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.057015image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.346476image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.641128image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.862693image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.078714image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.327315image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:10.857667image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.098989image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.378338image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.673348image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.893767image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.109639image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.361573image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:10.890656image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.137416image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.412017image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.703798image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.925819image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.141553image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.393860image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:10.923498image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.178777image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.444497image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.735575image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.955768image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.172491image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.423655image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:10.958190image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.215491image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.546790image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.766192image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:11.986534image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-02-28T16:31:12.201105image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-02-28T16:31:14.386632image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Bias Motive DescriptionComplaint Precinct CodeComplaint Year NumberCountyFull Complaint IDLaw Code Category DescriptionMonth NumberOffense CategoryOffense DescriptionPatrol Borough NameUnnamed: 0Unnamed: 0.1Unnamed: 0.2
Bias Motive Description1.0000.1460.1700.1610.1510.1450.1170.9210.1490.1610.0970.0970.097
Complaint Precinct Code0.1461.000-0.0200.8610.1600.0540.0180.1000.0910.7930.0550.0550.055
Complaint Year Number0.170-0.0201.0000.0660.9830.045-0.0540.1150.0870.0640.2530.2530.253
County0.1610.8610.0661.0000.3920.0360.0710.0960.0811.0000.1960.1960.196
Full Complaint ID0.1510.1600.9830.3921.0000.040-0.0490.1060.0760.3150.2600.2600.260
Law Code Category Description0.1450.0540.0450.0360.0401.0000.0310.1470.9670.0590.0440.0440.044
Month Number0.1170.018-0.0540.071-0.0490.0311.0000.0910.0530.0620.0230.0230.023
Offense Category0.9210.1000.1150.0960.1060.1470.0911.0000.2100.0970.0820.0820.082
Offense Description0.1490.0910.0870.0810.0760.9670.0530.2101.0000.0990.1260.1260.126
Patrol Borough Name0.1610.7930.0641.0000.3150.0590.0620.0970.0991.0000.1520.1520.152
Unnamed: 00.0970.0550.2530.1960.2600.0440.0230.0820.1260.1521.0001.0001.000
Unnamed: 0.10.0970.0550.2530.1960.2600.0440.0230.0820.1260.1521.0001.0001.000
Unnamed: 0.20.0970.0550.2530.1960.2600.0440.0230.0820.1260.1521.0001.0001.000

Missing values

2025-02-28T16:31:12.521000image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-28T16:31:12.574232image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-28T16:31:12.866255image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0.2Unnamed: 0.1Unnamed: 0Full Complaint IDComplaint Year NumberMonth NumberRecord Create DateComplaint Precinct CodePatrol Borough NameCountyLaw Code Category DescriptionOffense DescriptionPD Code DescriptionBias Motive DescriptionOffense CategoryArrest DateArrest Id
00002021050122458172021505/01/202150PATROL BORO BRONXBRONXFELONYBURGLARYBURGLARY,UNCLASSIFIED,NIGHTANTI-JEWISHReligion/Religious Practice05/01/2021B33683676
111120210501266831720211212/28/202150PATROL BORO BRONXBRONXFELONYMISCELLANEOUS PENAL LAWAGGRAVATED HARASSMENT 1ANTI-JEWISHReligion/Religious Practice09/28/2022B34705870
222220220491279211720221010/11/202249PATROL BORO BRONXBRONXFELONYFELONY ASSAULTASSAULT 2,1,UNCLASSIFIEDANTI-MALE HOMOSEXUAL (GAY)Sexual Orientation10/11/2022B34707656
33332019061121010172019101/15/201961PATROL BORO BKLYN SOUTHKINGSFELONYMURDER & NON-NEGL. MANSLAUGHTEMURDER,UNCLASSIFIEDANTI-ASIANRace/Color01/16/2019K31675023
44442019071121481172019202/08/201971PATROL BORO BKLYN SOUTHKINGSMISDEMEANOROFF. AGNST PUB ORD SENSBLTY &AGGRAVATED HARASSMENT 2ANTI-JEWISHReligion/Religious Practice02/08/2019K31679592
55552019094121887172019404/06/201994PATROL BORO BKLYN NORTHKINGSFELONYMISCELLANEOUS PENAL LAWAGGRAVATED HARASSMENT 1ANTI-JEWISHReligion/Religious Practice04/05/2019K31690190
66662019070122484172019303/17/201970PATROL BORO BKLYN SOUTHKINGSMISDEMEANORASSAULT 3 & RELATED OFFENSESASSAULT 3ANTI-MUSLIMReligion/Religious Practice04/07/2019K31690422
77772019079125985172019707/28/201979PATROL BORO BKLYN NORTHKINGSFELONYROBBERYROBBERY,PERSONAL ELECTRONIC DEVICEANTI-MALE HOMOSEXUAL (GAY)Sexual Orientation07/28/2019K31710604
88882019079125985172019707/28/201979PATROL BORO BKLYN NORTHKINGSFELONYROBBERYROBBERY,PERSONAL ELECTRONIC DEVICEANTI-MALE HOMOSEXUAL (GAY)Sexual Orientation07/28/2019K31710607
99992020070123719172020505/12/202070PATROL BORO BKLYN SOUTHKINGSFELONYMISCELLANEOUS PENAL LAWAGGRAVATED HARASSMENT 1ANTI-JEWISHReligion/Religious Practice05/12/2020K32688959
Unnamed: 0.2Unnamed: 0.1Unnamed: 0Full Complaint IDComplaint Year NumberMonth NumberRecord Create DateComplaint Precinct CodePatrol Borough NameCountyLaw Code Category DescriptionOffense DescriptionPD Code DescriptionBias Motive DescriptionOffense CategoryArrest DateArrest Id
32453245324532452024001125233172024606/06/20241PATROL BORO MAN SOUTHNEW YORKVIOLATIONHARRASSMENT 2HARASSMENT,SUBD 3,4,5ANTI-JEWISHReligion/Religious PracticeNaNNaN
32463246324632462024013126647172024707/30/202413PATROL BORO MAN SOUTHNEW YORKMISDEMEANORCRIMINAL MISCHIEF & RELATED OFCRIMINAL MISCHIEF 4TH, GRAFFITANTI-JEWISHReligion/Religious PracticeNaNNaN
32473247324732472024017121258172024602/07/202417PATROL BORO MAN SOUTHNEW YORKMISDEMEANOROFF. AGNST PUB ORD SENSBLTY &AGGRAVATED HARASSMENT 2ANTI-TRANSGENDERGenderNaNNaN
32483248324832482024024122093172024303/31/202424PATROL BORO MAN NORTHNEW YORKFELONYROBBERYROBBERY,PUBLIC PLACE INSIDEANTI-OTHER ETHNICITYEthnicity/National Origin/AncestryNaNNaN
32493249324932492024026121748172024404/04/202426PATROL BORO MAN NORTHNEW YORKFELONYMISCELLANEOUS PENAL LAWAGGRAVATED HARASSMENT 1ANTI-JEWISHReligion/Religious PracticeNaNNaN
32503250325032502024066123715172024506/06/202466PATROL BORO BKLYN SOUTHKINGSFELONYMISCELLANEOUS PENAL LAWAGGRAVATED HARASSMENT 1ANTI-JEWISHReligion/Religious PracticeNaNNaN
32513251325132512024068122176172024303/19/202468PATROL BORO BKLYN SOUTHKINGSFELONYCRIMINAL MISCHIEF & RELATED OFCRIMINAL MIS 2 & 3ANTI-JEWISHReligion/Religious PracticeNaNNaN
32523252325232522024084125874172024707/30/202484PATROL BORO BKLYN NORTHKINGSFELONYFELONY ASSAULTASSAULT 2,1,UNCLASSIFIEDANTI-MALE HOMOSEXUAL (GAY)Sexual OrientationNaNNaN
32533253325332532024104123798172024505/10/2024104PATROL BORO QUEENS NORTHQUEENSFELONYMISCELLANEOUS PENAL LAWAGGRAVATED HARASSMENT 1ANTI-MUSLIMReligion/Religious PracticeNaNNaN
32543254325432542024107125665172024707/21/2024107PATROL BORO QUEENS SOUTHQUEENSFELONYMISCELLANEOUS PENAL LAWAGGRAVATED HARASSMENT 1ANTI-JEWISHReligion/Religious PracticeNaNNaN